Many real-world applications involve the collection and management of data in multiple databases. Current methods for discovering patterns in multiple databases have difficulty in applying complex queries across databases. Several approaches exist that look to each database separately to discover patterns from each database that are then verified to determine if they meet a query. Sequential Pattern Verification (SPV) begins pattern mining from a seed database and then passes on the discovered patterns to a second database for verification. The sequential process repeats until patterns have been verified by all databases involved in a query. Parallel Pattern Mining (PPM) looks to each database individually to determine patterns. The discovered patterns are forwarded to a central location for verification against a query. SPV and PPM rely on pattern discovery individually from each database, where the mining process at each single database does not consider the existence of other databases (unless the patterns are then forwarded to another database for verification). Collaborative Pattern mining (CPM) generates length-l candidates from each database individually. All candidates from all databases are forwarded to a central location for candidate justification, such that only candidates satisfying certain conditions are redispatched to each database for the next round of pattern growing (length-l+1). This process repeats until no more candidates can be further generated. In another example, frequent pattern trees have been utilized to mine frequent patterns from single databases. Each of these three methods are Apriori-based techniques focused on single database mining. Many techniques for looking at data in multiple databases can only answer simple queries where each element of the query must explicitly specify one single database and a corresponding threshold value.